Power of a statistical test
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The power of a statistical test is the probability, assuming that the null hypothesis is false (i.e. an effect is significant) of obtaining a result that will allow the rejection of the null hypothesis, the correct answer has to be A. Choice D would be correct if it had 70 percent rather than 30 percent. Choice B is talking about a Type I error which does not correspond to power. Choice C would be correct if it said 30 percent rather than 70 percent.
There are four components that influence the power of a test:
- Sample size, or the number of units (e.g., people) accessible to the study
- Effect size, the difference between the means, divided by the standard deviation (i.e. 'sensitivity')
- Alpha level (significance level), or the probability that the observed result is due to chance
- Power, or the probability that you will observe a treatment effect when it occurs
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